



"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_      						=			get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
@require_tokenizers
class       snake_case_    (    __snake_case  ,unittest.TestCase					):
							__lowerCAmelCase            =       XLMRobertaTokenizer
							__lowerCAmelCase            =       XLMRobertaTokenizerFast
							__lowerCAmelCase            =       True
							__lowerCAmelCase            =       True






							def snake_case_	( self   ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									a_  :   List[str]			    =  XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE      ,  keep_accents=__SCREAMING_SNAKE_CASE   )
									tokenizer.save_pretrained(self.tmpdirname   )






							def snake_case_	( self   ):
									a_  :   List[str]			    =  "<pad>"
									a_  :   Tuple			    =  1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE   )      ,  __SCREAMING_SNAKE_CASE   )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE   )      ,  __SCREAMING_SNAKE_CASE   )






							def snake_case_	( self   ):
									a_  :   Tuple			    =  list(self.get_tokenizer().get_vocab().keys()   )

									self.assertEqual(vocab_keys[0]      ,  "<s>"   )
									self.assertEqual(vocab_keys[1]      ,  "<pad>"   )
									self.assertEqual(vocab_keys[-1]      ,  "<mask>"   )
									self.assertEqual(len(__SCREAMING_SNAKE_CASE   )      ,  1_0_0_2   )






							def snake_case_	( self   ):
									self.assertEqual(self.get_tokenizer().vocab_size      ,  1_0_0_2   )






							def snake_case_	( self   ):
									a_  :   str			    =  XLMRobertaTokenizer(__SCREAMING_SNAKE_CASE      ,  keep_accents=__SCREAMING_SNAKE_CASE   )

									a_  :   Tuple			    =  tokenizer.tokenize("This is a test"   )
									self.assertListEqual(__SCREAMING_SNAKE_CASE      ,  ["▁This", "▁is", "▁a", "▁t", "est"]   )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )      ,  [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]      ,  )

									a_  :   List[Any]			    =  tokenizer.tokenize("I was born in 92000, and this is falsé."   )
									self.assertListEqual(
									    __SCREAMING_SNAKE_CASE      ,  [
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "9",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "é",
									        ".",
									    ]      ,  )
									a_  :   Union[str, Any]			    =  tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )
									self.assertListEqual(
									    __SCREAMING_SNAKE_CASE      ,  [
									        value + tokenizer.fairseq_offset
									        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
									        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
									    ]      ,  )

									a_  :   Tuple			    =  tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE   )
									self.assertListEqual(
									    __SCREAMING_SNAKE_CASE      ,  [
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "<unk>",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "<unk>",
									        ".",
									    ]      ,  )






							def snake_case_	( self   ):
									if not self.test_slow_tokenizer:
											# as we don't have a slow version, we can't compare the outputs between slow and fast versions
											return

									a_  :   Tuple			    =  (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
													a_  :   Optional[int]			    =  self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE      ,  **__SCREAMING_SNAKE_CASE   )
													a_  :   List[str]			    =  self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE      ,  **__SCREAMING_SNAKE_CASE   )

													a_  :   str			    =  tempfile.mkdtemp()

													a_  :   Optional[int]			    =  tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE   )
													a_  :   Union[str, Any]			    =  tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE   )

													# Checks it save with the same files + the tokenizer.json file for the fast one
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )
													a_  :   List[str]			    =  tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
													self.assertSequenceEqual(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

													# Checks everything loads correctly in the same way
													a_  :   List[str]			    =  tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE   )
													a_  :   List[Any]			    =  tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE   )

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )   )
															# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
															# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

													shutil.rmtree(__SCREAMING_SNAKE_CASE   )

													# Save tokenizer rust, legacy_format=True
													a_  :   Dict			    =  tempfile.mkdtemp()

													a_  :   Tuple			    =  tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE      ,  legacy_format=__SCREAMING_SNAKE_CASE   )
													a_  :   Any			    =  tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE   )

													# Checks it save with the same files
													self.assertSequenceEqual(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

													# Checks everything loads correctly in the same way
													a_  :   Dict			    =  tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE   )
													a_  :   List[Any]			    =  tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE   )

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )   )

													shutil.rmtree(__SCREAMING_SNAKE_CASE   )

													# Save tokenizer rust, legacy_format=False
													a_  :   List[Any]			    =  tempfile.mkdtemp()

													a_  :   str			    =  tokenizer_r.save_pretrained(__SCREAMING_SNAKE_CASE      ,  legacy_format=__SCREAMING_SNAKE_CASE   )
													a_  :   Optional[Any]			    =  tokenizer_p.save_pretrained(__SCREAMING_SNAKE_CASE   )

													# Checks it saved the tokenizer.json file
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )

													# Checks everything loads correctly in the same way
													a_  :   str			    =  tokenizer_r.from_pretrained(__SCREAMING_SNAKE_CASE   )
													a_  :   str			    =  tokenizer_p.from_pretrained(__SCREAMING_SNAKE_CASE   )

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
															self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )   )

													shutil.rmtree(__SCREAMING_SNAKE_CASE   )






							@cached_property
							def snake_case_	( self   ):
									return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"   )






							def snake_case_	( self   ):
									with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(__SCREAMING_SNAKE_CASE      ,  f.name   )
											a_  :   List[Any]			    =  XLMRobertaTokenizer(f.name      ,  keep_accents=__SCREAMING_SNAKE_CASE   )
											a_  :   List[Any]			    =  pickle.dumps(__SCREAMING_SNAKE_CASE   )
									pickle.loads(__SCREAMING_SNAKE_CASE   )






							def snake_case_	( self   ):
									if not self.test_rust_tokenizer:
											return

									a_  :   Tuple			    =  self.get_tokenizer()
									a_  :   Tuple			    =  self.get_rust_tokenizer()

									a_  :   Dict			    =  "I was born in 92000, and this is falsé."

									a_  :   Dict			    =  tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
									a_  :   Optional[Any]			    =  rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
									self.assertListEqual(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

									a_  :   Optional[Any]			    =  tokenizer.encode(__SCREAMING_SNAKE_CASE      ,  add_special_tokens=__SCREAMING_SNAKE_CASE   )
									a_  :   Any			    =  rust_tokenizer.encode(__SCREAMING_SNAKE_CASE      ,  add_special_tokens=__SCREAMING_SNAKE_CASE   )
									self.assertListEqual(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

									a_  :   int			    =  self.get_rust_tokenizer()
									a_  :   int			    =  tokenizer.encode(__SCREAMING_SNAKE_CASE   )
									a_  :   Optional[int]			    =  rust_tokenizer.encode(__SCREAMING_SNAKE_CASE   )
									self.assertListEqual(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )






							@slow
							def snake_case_	( self   ):
									a_  :   int			    =  "Hello World!"
									a_  :   Optional[int]			    =  [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(__SCREAMING_SNAKE_CASE      ,  self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE   )   )






							@slow
							def snake_case_	( self   ):
									a_  :   Any			    =  (
									    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
									    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
									)
									a_  :   int			    =  [
									    0,
									    3_2_9_3,
									    8_3,
									    1_0,
									    4_5_5_2,
									    4_9_8_9,
									    7_9_8_6,
									    6_7_8,
									    1_0,
									    5_9_1_5,
									    1_1_1,
									    1_7_9_4_5_9,
									    1_2_4_8_5_0,
									    4,
									    6_0_4_4,
									    2_3_7,
									    1_2,
									    6,
									    5,
									    6,
									    4,
									    6_7_8_0,
									    7_0_5,
									    1_5,
									    1_3_8_8,
									    4_4,
									    3_7_8,
									    1_0_1_1_4,
									    7_1_1,
									    1_5_2,
									    2_0,
									    6,
									    5,
									    2_2_3_7_6,
									    6_4_2,
									    1_2_2_1,
									    1_5_1_9_0,
									    3_4_1_5_3,
									    4_5_0,
									    5_6_0_8,
									    9_5_9,
									    1_1_1_9,
									    5_7_7_0_2,
									    1_3_6,
									    1_8_6,
									    4_7,
									    1_0_9_8,
									    2_9_3_6_7,
									    4_7,
									    # 4426, # What fairseq tokenizes from "<unk>": "_<"
									    # 3678, # What fairseq tokenizes from "<unk>": "unk"
									    # 2740, # What fairseq tokenizes from "<unk>": ">"
									    3,  # What we tokenize from "<unk>": "<unk>"
									    6,  # Residue from the tokenization: an extra sentencepiece underline
									    4,
									    6_0_4_4,
									    2_3_7,
									    6_2_8_4,
									    5_0_9_0_1,
									    5_2_8,
									    3_1,
									    9_0,
									    3_4,
									    9_2_7,
									    2,
									]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(__SCREAMING_SNAKE_CASE      ,  self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE   )   )






							@slow
							def snake_case_	( self   ):
									# fmt: off
									a_  :   Union[str, Any]			    =  {"input_ids": [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=__SCREAMING_SNAKE_CASE      ,  model_name="xlm-roberta-base"      ,  revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"      ,  )







"""simple docstring"""





from scipy.stats import pearsonr

import datasets


_SCREAMING_SNAKE_CASE   :							List[str]          =		'''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


_SCREAMING_SNAKE_CASE   :							int          =		'''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


_SCREAMING_SNAKE_CASE   :							Dict          =		'''
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    , _KWARGS_DESCRIPTION						)
class 	a		(   datasets.Metric						):






    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      return datasets.MetricInfo(
          description=_DESCRIPTION			,		citation=_CITATION			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
              {
                  'predictions': datasets.Value('float'   ),
                  'references': datasets.Value('float'   ),
              }   )			,		reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html']			,		)






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Tuple=False   )	->						Tuple:
      if return_pvalue:
        lowerCamelCase_       	=		pearsonr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
        return {"pearsonr": results[0], "p-value": results[1]}
      else:
        return {"pearsonr": float(pearsonr(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )[0]   )}




